Adaptive dynamic RBF neural controller design for a class of nonlinear systems

被引:13
|
作者
Hsu, Chun-Fei [1 ]
机构
[1] Tamkang Univ, Dept Elect Engn, New Taipei City 25137, Taiwan
关键词
Adaptive control; Neural control; Lyapunov stability theorem; DC motor; Chaotic system; SLIDING-MODE CONTROL; INDUCTION-MOTOR DRIVE; BACKSTEPPING CONTROL; CHAOTIC SYSTEMS; NETWORK CONTROL; ROBUST; APPROXIMATION;
D O I
10.1016/j.asoc.2011.08.001
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
In this paper, an adaptive DRBF neural control (ADNC) system which is composed of a neural controller and a smooth compensator is proposed. The neural controller utilizes a dynamic radial basis function (DRBF) network to online mimic an ideal controller and the smooth compensator is designed to eliminate the effect of the approximation error between the ideal controller and neural controller. The DRBF network can self-organizing its network structure. All the controller parameters of the proposed ADNC system are online tuned in the Lyapunov sense, thus the stability analytic shows the system output can exponentially converge to a small neighborhood of the trajectory command. Finally, the proposed ADNC system is applied to a chaotic system and a DC motor. Simulation and experimental results verify that a favorable tracking performance and no chattering phenomena can be achieved by the proposed ADNC system. (C) 2011 Elsevier B.V. All rights reserved.
引用
收藏
页码:4607 / 4613
页数:7
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